Overview

Dataset statistics

Number of variables19
Number of observations28944
Missing cells42099
Missing cells (%)7.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.2 MiB
Average record size in memory152.0 B

Variable types

Unsupported3
Numeric8
Categorical3
DateTime5

Alerts

id is highly overall correlated with crmUserId and 2 other fieldsHigh correlation
crmId is highly overall correlated with crmOrgId and 1 other fieldsHigh correlation
crmUserId is highly overall correlated with id and 3 other fieldsHigh correlation
crmOrgId is highly overall correlated with crmId and 1 other fieldsHigh correlation
companyId is highly overall correlated with id and 2 other fieldsHigh correlation
crmValue is highly overall correlated with crmPipelineIdHigh correlation
crmCustomerId is highly overall correlated with crmId and 1 other fieldsHigh correlation
crmPipelineId is highly overall correlated with id and 3 other fieldsHigh correlation
crmValueCurrency is highly overall correlated with crmUserIdHigh correlation
isDeleted is highly imbalanced (88.9%)Imbalance
crmValueCurrency is highly imbalanced (87.0%)Imbalance
crmOrgId has 923 (3.2%) missing valuesMissing
crmWonTime has 25891 (89.5%) missing valuesMissing
crmCustomerId has 5907 (20.4%) missing valuesMissing
crmLostTime has 9124 (31.5%) missing valuesMissing
id is uniformly distributedUniform
id has unique valuesUnique
_id is an unsupported type, check if it needs cleaning or further analysisUnsupported
crmTitle is an unsupported type, check if it needs cleaning or further analysisUnsupported
crmFormattedWeightedValue is an unsupported type, check if it needs cleaning or further analysisUnsupported
crmValue has 10484 (36.2%) zerosZeros

Reproduction

Analysis started2023-06-20 10:33:13.265369
Analysis finished2023-06-20 10:33:22.677421
Duration9.41 seconds
Software versionydata-profiling vv4.2.0
Download configurationconfig.json

Variables

_id
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size226.2 KiB

id
Real number (ℝ)

HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct28944
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14540.191
Minimum1
Maximum29084
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size226.2 KiB
2023-06-20T12:33:22.745265image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1448.15
Q17236.75
median14585.5
Q321828.25
95-th percentile27621.85
Maximum29084
Range29083
Interquartile range (IQR)14591.5

Descriptive statistics

Standard deviation8407.2488
Coefficient of variation (CV)0.57820758
Kurtosis-1.2051592
Mean14540.191
Median Absolute Deviation (MAD)7296
Skewness-0.0016027691
Sum4.2085129 × 108
Variance70681832
MonotonicityStrictly increasing
2023-06-20T12:33:22.882498image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
19395 1
 
< 0.1%
19421 1
 
< 0.1%
19420 1
 
< 0.1%
19419 1
 
< 0.1%
19418 1
 
< 0.1%
19417 1
 
< 0.1%
19416 1
 
< 0.1%
19415 1
 
< 0.1%
19414 1
 
< 0.1%
Other values (28934) 28934
> 99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
29084 1
< 0.1%
29083 1
< 0.1%
29082 1
< 0.1%
29081 1
< 0.1%
29080 1
< 0.1%
29079 1
< 0.1%
29078 1
< 0.1%
29077 1
< 0.1%
29076 1
< 0.1%
29075 1
< 0.1%

crmId
Real number (ℝ)

Distinct14374
Distinct (%)49.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5596.6884
Minimum1
Maximum18140
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size226.2 KiB
2023-06-20T12:33:23.455181image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile155
Q11175
median4117.5
Q38067
95-th percentile16608.85
Maximum18140
Range18139
Interquartile range (IQR)6892

Descriptive statistics

Standard deviation5149.5815
Coefficient of variation (CV)0.92011223
Kurtosis-0.27131536
Mean5596.6884
Median Absolute Deviation (MAD)3360.5
Skewness0.88955501
Sum1.6199055 × 108
Variance26518190
MonotonicityNot monotonic
2023-06-20T12:33:23.590332image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
245 14
 
< 0.1%
261 13
 
< 0.1%
332 13
 
< 0.1%
342 13
 
< 0.1%
341 13
 
< 0.1%
30 13
 
< 0.1%
33 13
 
< 0.1%
336 13
 
< 0.1%
335 13
 
< 0.1%
331 13
 
< 0.1%
Other values (14364) 28813
99.5%
ValueCountFrequency (%)
1 12
< 0.1%
2 13
< 0.1%
3 11
< 0.1%
4 10
< 0.1%
5 12
< 0.1%
6 12
< 0.1%
7 11
< 0.1%
8 9
< 0.1%
9 13
< 0.1%
10 11
< 0.1%
ValueCountFrequency (%)
18140 1
< 0.1%
18139 1
< 0.1%
18138 1
< 0.1%
18137 1
< 0.1%
18136 1
< 0.1%
18135 1
< 0.1%
18134 1
< 0.1%
18133 1
< 0.1%
18132 1
< 0.1%
18131 1
< 0.1%

crmUserId
Real number (ℝ)

Distinct107
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10390270
Minimum519660
Maximum15168007
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size226.2 KiB
2023-06-20T12:33:23.731330image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum519660
5-th percentile1312133
Q19621004
median11861022
Q312182940
95-th percentile13410041
Maximum15168007
Range14648347
Interquartile range (IQR)2561936

Descriptive statistics

Standard deviation3624417.1
Coefficient of variation (CV)0.34882798
Kurtosis1.4741446
Mean10390270
Median Absolute Deviation (MAD)530494
Skewness-1.6288246
Sum3.0073599 × 1011
Variance1.3136399 × 1013
MonotonicityNot monotonic
2023-06-20T12:33:23.860329image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12164637 1792
 
6.2%
13188567 1557
 
5.4%
11490668 1523
 
5.3%
9621004 1237
 
4.3%
1726230 1222
 
4.2%
11353976 1202
 
4.2%
11924103 1093
 
3.8%
11490788 953
 
3.3%
12182940 925
 
3.2%
11330528 923
 
3.2%
Other values (97) 16517
57.1%
ValueCountFrequency (%)
519660 750
2.6%
632315 16
 
0.1%
686380 100
 
0.3%
944589 7
 
< 0.1%
1194982 11
 
< 0.1%
1312133 630
2.2%
1347043 9
 
< 0.1%
1349827 10
 
< 0.1%
1726230 1222
4.2%
1954927 162
 
0.6%
ValueCountFrequency (%)
15168007 361
1.2%
14344370 1
 
< 0.1%
14297763 1
 
< 0.1%
14284464 22
 
0.1%
14211842 66
 
0.2%
14115845 5
 
< 0.1%
14115834 6
 
< 0.1%
13755914 8
 
< 0.1%
13755903 9
 
< 0.1%
13755892 3
 
< 0.1%

crmOrgId
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct15513
Distinct (%)55.4%
Missing923
Missing (%)3.2%
Infinite0
Infinite (%)0.0%
Mean15220.434
Minimum1
Maximum48788
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size226.2 KiB
2023-06-20T12:33:23.994387image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile65
Q11920
median11148
Q326392
95-th percentile43001
Maximum48788
Range48787
Interquartile range (IQR)24472

Descriptive statistics

Standard deviation14103.764
Coefficient of variation (CV)0.92663353
Kurtosis-0.85494736
Mean15220.434
Median Absolute Deviation (MAD)9883
Skewness0.65294658
Sum4.2649178 × 108
Variance1.9891617 × 108
MonotonicityNot monotonic
2023-06-20T12:33:24.124549image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 60
 
0.2%
3 43
 
0.1%
8 42
 
0.1%
42 37
 
0.1%
16 36
 
0.1%
528 36
 
0.1%
30 35
 
0.1%
84 35
 
0.1%
5 31
 
0.1%
46 31
 
0.1%
Other values (15503) 27635
95.5%
(Missing) 923
 
3.2%
ValueCountFrequency (%)
1 60
0.2%
2 11
 
< 0.1%
3 43
0.1%
4 23
 
0.1%
5 31
0.1%
6 19
 
0.1%
7 25
0.1%
8 42
0.1%
9 25
0.1%
10 29
0.1%
ValueCountFrequency (%)
48788 3
< 0.1%
48787 1
 
< 0.1%
48786 1
 
< 0.1%
48785 1
 
< 0.1%
48781 2
< 0.1%
48780 1
 
< 0.1%
48779 1
 
< 0.1%
48778 1
 
< 0.1%
48777 1
 
< 0.1%
48776 2
< 0.1%

crmTitle
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size226.2 KiB

crmStatus
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size226.2 KiB
lost
19825 
open
6034 
won
3052 
deleted
 
33

Length

Max length7
Median length4
Mean length3.8979754
Min length3

Characters and Unicode

Total characters112823
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowlost
2nd rowlost
3rd rowwon
4th rowwon
5th rowlost

Common Values

ValueCountFrequency (%)
lost 19825
68.5%
open 6034
 
20.8%
won 3052
 
10.5%
deleted 33
 
0.1%

Length

2023-06-20T12:33:24.256425image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-20T12:33:24.378610image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
lost 19825
68.5%
open 6034
 
20.8%
won 3052
 
10.5%
deleted 33
 
0.1%

Most occurring characters

ValueCountFrequency (%)
o 28911
25.6%
l 19858
17.6%
t 19858
17.6%
s 19825
17.6%
n 9086
 
8.1%
e 6133
 
5.4%
p 6034
 
5.3%
w 3052
 
2.7%
d 66
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 112823
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 28911
25.6%
l 19858
17.6%
t 19858
17.6%
s 19825
17.6%
n 9086
 
8.1%
e 6133
 
5.4%
p 6034
 
5.3%
w 3052
 
2.7%
d 66
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 112823
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 28911
25.6%
l 19858
17.6%
t 19858
17.6%
s 19825
17.6%
n 9086
 
8.1%
e 6133
 
5.4%
p 6034
 
5.3%
w 3052
 
2.7%
d 66
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 112823
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 28911
25.6%
l 19858
17.6%
t 19858
17.6%
s 19825
17.6%
n 9086
 
8.1%
e 6133
 
5.4%
p 6034
 
5.3%
w 3052
 
2.7%
d 66
 
0.1%
Distinct2807
Distinct (%)91.9%
Missing25891
Missing (%)89.5%
Memory size226.2 KiB
Minimum2015-03-12 12:35:15
Maximum2022-06-08 15:00:30
2023-06-20T12:33:24.484839image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T12:33:24.628971image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct11896
Distinct (%)41.1%
Missing0
Missing (%)0.0%
Memory size226.2 KiB
Minimum2017-12-11 11:34:22
Maximum2022-06-09 06:13:11
2023-06-20T12:33:24.755682image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T12:33:24.878864image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct24428
Distinct (%)84.4%
Missing0
Missing (%)0.0%
Memory size226.2 KiB
Minimum2015-03-03 17:01:37
Maximum2022-06-08 17:35:00
2023-06-20T12:33:25.021053image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T12:33:25.150842image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

crmFormattedWeightedValue
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size226.2 KiB

isDeleted
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size226.2 KiB
0
28517 
1
 
427

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters28944
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 28517
98.5%
1 427
 
1.5%

Length

2023-06-20T12:33:25.261121image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-20T12:33:25.359342image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 28517
98.5%
1 427
 
1.5%

Most occurring characters

ValueCountFrequency (%)
0 28517
98.5%
1 427
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 28944
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 28517
98.5%
1 427
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
Common 28944
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 28517
98.5%
1 427
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28944
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 28517
98.5%
1 427
 
1.5%
Distinct1836
Distinct (%)6.3%
Missing0
Missing (%)0.0%
Memory size226.2 KiB
Minimum2022-01-27 14:13:05
Maximum2022-06-08 17:35:13
2023-06-20T12:33:25.454601image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T12:33:25.584822image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

companyId
Real number (ℝ)

Distinct18
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.8009605
Minimum2
Maximum42
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size226.2 KiB
2023-06-20T12:33:25.702700image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile5
Q15
median8
Q316
95-th percentile16
Maximum42
Range40
Interquartile range (IQR)11

Descriptive statistics

Standard deviation5.6686188
Coefficient of variation (CV)0.5783738
Kurtosis3.6177047
Mean9.8009605
Median Absolute Deviation (MAD)3
Skewness1.4041138
Sum283679
Variance32.133239
MonotonicityNot monotonic
2023-06-20T12:33:25.813951image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
5 10491
36.2%
16 7816
27.0%
12 2222
 
7.7%
8 2062
 
7.1%
9 2029
 
7.0%
6 1031
 
3.6%
7 792
 
2.7%
35 402
 
1.4%
14 361
 
1.2%
11 340
 
1.2%
Other values (8) 1398
 
4.8%
ValueCountFrequency (%)
2 340
 
1.2%
3 340
 
1.2%
4 340
 
1.2%
5 10491
36.2%
6 1031
 
3.6%
7 792
 
2.7%
8 2062
 
7.1%
9 2029
 
7.0%
11 340
 
1.2%
12 2222
 
7.7%
ValueCountFrequency (%)
42 13
 
< 0.1%
35 402
 
1.4%
21 12
 
< 0.1%
19 1
 
< 0.1%
18 340
 
1.2%
16 7816
27.0%
14 361
 
1.2%
13 12
 
< 0.1%
12 2222
 
7.7%
11 340
 
1.2%

crmValue
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct3071
Distinct (%)10.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26368.683
Minimum0
Maximum1638000
Zeros10484
Zeros (%)36.2%
Negative0
Negative (%)0.0%
Memory size226.2 KiB
2023-06-20T12:33:25.946681image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4250.5
Q330000
95-th percentile120000
Maximum1638000
Range1638000
Interquartile range (IQR)30000

Descriptive statistics

Standard deviation68785.343
Coefficient of variation (CV)2.6085999
Kurtosis119.11331
Mean26368.683
Median Absolute Deviation (MAD)4250.5
Skewness8.540317
Sum7.6321516 × 108
Variance4.7314235 × 109
MonotonicityNot monotonic
2023-06-20T12:33:26.091838image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 10484
36.2%
30000 3658
 
12.6%
36000 3201
 
11.1%
42000 462
 
1.6%
7164 351
 
1.2%
3564 213
 
0.7%
800 210
 
0.7%
14364 172
 
0.6%
10000 163
 
0.6%
68 134
 
0.5%
Other values (3061) 9896
34.2%
ValueCountFrequency (%)
0 10484
36.2%
1 88
 
0.3%
2 6
 
< 0.1%
3 6
 
< 0.1%
4 1
 
< 0.1%
5 2
 
< 0.1%
6 1
 
< 0.1%
7 1
 
< 0.1%
10 1
 
< 0.1%
12 1
 
< 0.1%
ValueCountFrequency (%)
1638000 2
< 0.1%
1495200 2
< 0.1%
1388400 2
< 0.1%
1362000 4
< 0.1%
1281600 2
< 0.1%
1141308 4
< 0.1%
1088400 2
< 0.1%
1014600 2
< 0.1%
993600 2
< 0.1%
985764 2
< 0.1%

crmValueCurrency
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing2
Missing (%)< 0.1%
Memory size226.2 KiB
EUR
28419 
USD
 
523

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters86826
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEUR
2nd rowEUR
3rd rowEUR
4th rowEUR
5th rowEUR

Common Values

ValueCountFrequency (%)
EUR 28419
98.2%
USD 523
 
1.8%
(Missing) 2
 
< 0.1%

Length

2023-06-20T12:33:26.211037image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-20T12:33:26.312281image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
eur 28419
98.2%
usd 523
 
1.8%

Most occurring characters

ValueCountFrequency (%)
U 28942
33.3%
E 28419
32.7%
R 28419
32.7%
S 523
 
0.6%
D 523
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 86826
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
U 28942
33.3%
E 28419
32.7%
R 28419
32.7%
S 523
 
0.6%
D 523
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 86826
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
U 28942
33.3%
E 28419
32.7%
R 28419
32.7%
S 523
 
0.6%
D 523
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 86826
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
U 28942
33.3%
E 28419
32.7%
R 28419
32.7%
S 523
 
0.6%
D 523
 
0.6%

crmCustomerId
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct16530
Distinct (%)71.8%
Missing5907
Missing (%)20.4%
Infinite0
Infinite (%)0.0%
Mean20568.551
Minimum1
Maximum50852
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size226.2 KiB
2023-06-20T12:33:26.410531image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile470
Q17811
median21613
Q329803
95-th percentile45754.8
Maximum50852
Range50851
Interquartile range (IQR)21992

Descriptive statistics

Standard deviation14015.495
Coefficient of variation (CV)0.68140409
Kurtosis-0.91295644
Mean20568.551
Median Absolute Deviation (MAD)10150
Skewness0.20382215
Sum4.738377 × 108
Variance1.9643409 × 108
MonotonicityNot monotonic
2023-06-20T12:33:26.544200image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 33
 
0.1%
2405 25
 
0.1%
2332 19
 
0.1%
35081 17
 
0.1%
24727 17
 
0.1%
13812 16
 
0.1%
3 14
 
< 0.1%
22387 14
 
< 0.1%
603 13
 
< 0.1%
118 12
 
< 0.1%
Other values (16520) 22857
79.0%
(Missing) 5907
 
20.4%
ValueCountFrequency (%)
1 33
0.1%
2 4
 
< 0.1%
3 14
< 0.1%
4 8
 
< 0.1%
5 11
 
< 0.1%
6 7
 
< 0.1%
7 7
 
< 0.1%
8 7
 
< 0.1%
9 7
 
< 0.1%
10 10
 
< 0.1%
ValueCountFrequency (%)
50852 1
< 0.1%
50851 1
< 0.1%
50850 1
< 0.1%
50842 1
< 0.1%
50832 1
< 0.1%
50821 1
< 0.1%
50820 1
< 0.1%
50814 1
< 0.1%
50813 1
< 0.1%
50812 1
< 0.1%
Distinct13938
Distinct (%)70.3%
Missing9124
Missing (%)31.5%
Memory size226.2 KiB
Minimum2015-04-28 14:47:15
Maximum2022-06-08 15:41:26
2023-06-20T12:33:26.669378image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T12:33:26.795926image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

crmPipelineId
Real number (ℝ)

Distinct15
Distinct (%)0.1%
Missing252
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean5.736024
Minimum1
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size226.2 KiB
2023-06-20T12:33:26.899221image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median5
Q38
95-th percentile18
Maximum20
Range19
Interquartile range (IQR)7

Descriptive statistics

Standard deviation5.1384398
Coefficient of variation (CV)0.8958191
Kurtosis0.88193255
Mean5.736024
Median Absolute Deviation (MAD)4
Skewness1.0929296
Sum164578
Variance26.403564
MonotonicityNot monotonic
2023-06-20T12:33:27.000929image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
1 11483
39.7%
8 6960
24.0%
9 3936
 
13.6%
5 1368
 
4.7%
20 1042
 
3.6%
18 1031
 
3.6%
2 967
 
3.3%
7 579
 
2.0%
3 428
 
1.5%
4 261
 
0.9%
Other values (5) 637
 
2.2%
(Missing) 252
 
0.9%
ValueCountFrequency (%)
1 11483
39.7%
2 967
 
3.3%
3 428
 
1.5%
4 261
 
0.9%
5 1368
 
4.7%
6 198
 
0.7%
7 579
 
2.0%
8 6960
24.0%
9 3936
 
13.6%
11 61
 
0.2%
ValueCountFrequency (%)
20 1042
 
3.6%
19 113
 
0.4%
18 1031
 
3.6%
16 63
 
0.2%
12 202
 
0.7%
11 61
 
0.2%
9 3936
13.6%
8 6960
24.0%
7 579
 
2.0%
6 198
 
0.7%

Interactions

2023-06-20T12:33:20.877936image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T12:33:14.639489image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T12:33:15.501761image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T12:33:16.378244image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T12:33:17.313014image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T12:33:18.163147image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T12:33:19.048860image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T12:33:19.933719image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T12:33:21.003111image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T12:33:14.755709image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T12:33:15.605996image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T12:33:16.488458image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T12:33:17.426739image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T12:33:18.274356image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T12:33:19.157083image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T12:33:20.047530image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T12:33:21.138776image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T12:33:14.862857image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T12:33:15.714788image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T12:33:16.602641image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T12:33:17.535961image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T12:33:18.384358image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T12:33:19.265305image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T12:33:20.161733image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T12:33:21.265955image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T12:33:14.972575image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T12:33:15.828001image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T12:33:16.715620image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T12:33:17.645237image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T12:33:18.498127image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T12:33:19.376630image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T12:33:20.284920image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T12:33:21.385149image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T12:33:15.073820image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T12:33:15.929241image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T12:33:16.831326image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T12:33:17.748058image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T12:33:18.609340image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T12:33:19.480867image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T12:33:20.391706image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T12:33:21.505339image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T12:33:15.184039image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T12:33:16.046599image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T12:33:16.959496image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T12:33:17.857271image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T12:33:18.721168image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T12:33:19.595099image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T12:33:20.518937image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T12:33:21.622567image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T12:33:15.291295image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T12:33:16.159540image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T12:33:17.072709image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T12:33:17.961500image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T12:33:18.830418image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T12:33:19.708800image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T12:33:20.628724image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T12:33:21.731809image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T12:33:15.391541image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T12:33:16.266497image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T12:33:17.195895image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T12:33:18.056834image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T12:33:18.936646image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T12:33:19.816021image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-20T12:33:20.732254image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-06-20T12:33:27.104229image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
idcrmIdcrmUserIdcrmOrgIdcompanyIdcrmValuecrmCustomerIdcrmPipelineIdcrmStatusisDeletedcrmValueCurrency
id1.000-0.1980.8020.0650.8510.4080.025-0.5850.2970.1650.305
crmId-0.1981.000-0.2140.674-0.303-0.1390.7420.4240.2010.3130.178
crmUserId0.802-0.2141.0000.0470.6530.4180.019-0.6190.2620.0820.625
crmOrgId0.0650.6740.0471.0000.045-0.0130.7590.0800.1630.2850.122
companyId0.851-0.3030.6530.0451.0000.384-0.117-0.5640.2080.0790.356
crmValue0.408-0.1390.418-0.0130.3841.0000.164-0.5100.0790.0150.020
crmCustomerId0.0250.7420.0190.759-0.1170.1641.0000.0300.1930.1370.214
crmPipelineId-0.5850.424-0.6190.080-0.564-0.5100.0301.0000.3000.0830.161
crmStatus0.2970.2010.2620.1630.2080.0790.1930.3001.0000.2370.256
isDeleted0.1650.3130.0820.2850.0790.0150.1370.0830.2371.0000.051
crmValueCurrency0.3050.1780.6250.1220.3560.0200.2140.1610.2560.0511.000

Missing values

2023-06-20T12:33:21.949327image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-06-20T12:33:22.281634image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-06-20T12:33:22.553243image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

_ididcrmIdcrmUserIdcrmOrgIdcrmTitlecrmStatuscrmWonTimecrmUpdateTimecrmAddTimecrmFormattedWeightedValueisDeletedcreateDatecompanyIdcrmValuecrmValueCurrencycrmCustomerIdcrmLostTimecrmPipelineId
064902efa7cc8b1037a60e09c11172623078.0LOST - IGEL GmbH - BASIC42341lostNone2021-12-22 15:07:402015-03-03 17:01:3724 €02022-01-27 14:13:055120EUR119.02015-12-03 13:55:388.0
164902efa7cc8b1037a60e09d22745691371.0OUT - Holy AGlostNone2019-12-20 16:32:522015-03-04 07:48:100 €02022-01-27 14:13:0550EURNaN2019-12-20 16:32:389.0
264902efa7cc8b1037a60e09e3351966046.0Jost Hurler - Schwabinger Tor Appwon2015-06-19 09:58:242021-05-20 16:54:312015-03-04 08:46:1360.000 €02022-01-27 14:13:05560000EUR109.0None8.0
364902efa7cc8b1037a60e09f4451966068.0etcos GmbH - Pilotprogrammwon2015-03-12 12:35:152021-05-20 16:55:452015-03-04 08:49:210 €02022-01-27 14:13:0550EUR108.0None8.0
464902efa7cc8b1037a60e0a055962100488.0LOST - Wagnis e.G. - Wohnportal42122lostNone2021-12-22 15:17:372015-03-04 08:51:0740 €02022-01-27 14:13:055200EUR27.02015-04-28 14:47:158.0
564902efa7cc8b1037a60e0a1665196604.0Dr. Gerlich GmbH - Rolloutwon2015-09-02 15:43:232021-05-20 16:56:062015-03-04 08:52:262.700 €02022-01-27 14:13:0552700EUR88.0None8.0
664902efa7cc8b1037a60e0a27751966012484.0YOUNIQ AG - Pilotprogrammwon2015-05-23 13:52:282021-05-20 16:57:272015-03-04 08:55:490 €02022-01-27 14:13:0550EUR30.0None8.0
764902efa7cc8b1037a60e0a38968638053.0VfFK - Rolloutwon2015-10-30 16:41:342021-05-20 16:53:592015-03-04 09:00:21120 €02022-01-27 14:13:055120EUR84.0None8.0
864902efa7cc8b1037a60e0a491096210042728.0LOST - Studiosus Studentenapartments - Outreach42591lostNone2021-12-22 15:17:162015-03-04 09:32:340 €02022-01-27 14:13:0550EUR3562.02016-08-09 08:07:248.0
964902efa7cc8b1037a60e0a51012519660138.0OUT - Südhausbau KGwon2018-11-23 14:40:402021-12-16 12:27:512015-03-05 10:17:1815.444 €02022-01-27 14:13:05515444EUR22437.0None8.0
_ididcrmIdcrmUserIdcrmOrgIdcrmTitlecrmStatuscrmWonTimecrmUpdateTimecrmAddTimecrmFormattedWeightedValueisDeletedcreateDatecompanyIdcrmValuecrmValueCurrencycrmCustomerIdcrmLostTimecrmPipelineId
2893464902efb7cc8b1037a6151a229075181331204262716654.0Aschoff Immobilien Ltd. dealopenNone2022-06-08 12:55:422022-06-08 12:47:06712,80 €02022-06-08 12:48:5353564EUR21111.0None8.0
2893564902efb7cc8b1037a6151a329076181341196779533901.0Werner Sutter & Co. AGopenNone2022-06-08 13:06:162022-06-08 12:56:574.309,20 €02022-06-08 12:57:02514364EUR48523.0None8.0
2893664902efb7cc8b1037a6151a429077181361196779518517.0LMI Immobilien dealopenNone2022-06-08 13:36:222022-06-08 13:28:151.432,80 €02022-06-08 13:28:1757164EUR50850.0None8.0
2893764902efb7cc8b1037a6151a529078181371265441835556.0Euro Fashion Center Verwaltungs GmbH dealopenNone2022-06-08 15:20:102022-06-08 13:34:190 €02022-06-08 13:34:2050EUR50852.0None8.0
2893864902efb7cc8b1037a6151a629079181381375589220330.0Baugenossenschaft des Landkreises Coburg eG WOWIPORT (Frank & Viktoria)openNone2022-06-08 14:12:042022-06-08 13:42:309.196,20 €02022-06-08 13:42:48530654EUR48216.0None8.0
2893964902efb7cc8b1037a6151a729080181391375591435557.0IN - ST.SH Immobilienservice GmbHopenNone2022-06-08 14:50:052022-06-08 14:16:070 €02022-06-08 14:16:1050EUR7523.0None8.0
2894064902efb7cc8b1037a6151a829081181401196779534823.0RE/MAX Immowest R. Götze GmbHopenNone2022-06-08 14:51:542022-06-08 14:42:571.069,20 €02022-06-08 14:51:2653564EUR48449.0None8.0
2894164902efb7cc8b1037a6151a9290821068125770021881.0Deecoob Technology Gmbh LeadopenNone2022-06-08 14:58:392022-06-08 14:58:37179 €02022-06-08 14:58:417179EUR1708.0None1.0
2894264902efb7cc8b1037a6151aa29083181351265441835555.0[(Chatbot) - ] LBBW Immobilien Asset Management GmbHopenNone2022-06-08 15:52:502022-06-08 13:15:490 €02022-06-08 15:35:4550EUR50851.0None8.0
2894364902efb7cc8b1037a6151ab290842923120248439175.0marta GmbH DealopenNone2022-06-09 06:13:112022-06-08 17:35:000 €02022-06-08 17:35:13120EUR19971.0None7.0